Mapping Symptom Clusters to Circuits: Toward Personalizing TMS Targets to Improve Treatment Outcomes in Depression

Transcriptomic Similarity Informs Neuromorphic Deviations in Depression Biotypes

BACKGROUND

Major depressive disorder (MDD) is complicated by population heterogeneity, motivating the investigation of biotypes through imaging-derived phenotypes. However, neuromorphic heterogeneity in MDD remains unclear, and how the correlated gene expression (CGE) connectome constrains these neuromorphic anomalies in MDD biotypes has not yet been studied.

METHODS

Here, we related cortical thickness deviations in MDD biotypes to a pattern of CGE connectome. Cortical thickness was estimated from 3-dimensional T1-weighted magnetic resonance images in 2 independent cohorts (discovery cohort: N = 425; replication cohort: N = 217). The transcriptional activity was measured according to Allen Human Brain Atlas. A density peak-based clustering algorithm was used to identify MDD biotypes.

RESULTS

We found that patients with MDD were clustered into 2 replicated biotypes based on single-patient regional deviations from healthy control participants across 2 datasets. Biotype 1 mainly exhibited cortical thinning across the brain, whereas biotype 2 mainly showed cortical thickening in the brain. Using brainwide gene expression data, we found that deviations of transcriptionally connected neighbors predicted regional deviation for both biotypes. Furthermore, putative CGE-informed epicenters of biotype 1 were concentrated on the cognitive control circuit, whereas biotype 2 epicenters were located in the social perception circuit. The patterns of epicenter likelihood were separately associated with depression- and anxiety-response maps, suggesting that epicenters of MDD biotypes may be associated with clinical efficacies.

CONCLUSIONS

Our findings linked the CGE connectome and neuromorphic deviations to identify distinct epicenters in MDD biotypes, providing insight into how microscale gene expressions informed MDD biotypes.

A neuroimaging-based precision medicine framework for depression

BACKGROUND

Symptom-based diagnostic criteria of depression leads to notorious heterogeneity and subjectivity.

METHODS

The study was conducted in two stages at two sites: development of a neuroimaging-based subtyping and precise repetitive transcranial magnetic stimulation (rTMS) strategy for depression at Center 1 and its clinical application at Center 2. Center 1 identified depression subtypes and subtype-specific rTMS targets based on amplitude of low frequency fluctuation (ALFF) in a cohort of 238 major depressive disorder patients and 66 healthy controls (HC). Subtypes were identified using a Gaussian Mixture Model, and subtype-specific rTMS targets were selected based on dominant brain regions prominently differentiating depression subtypes from HC. Subsequently, one classifier was employed and 72 hospitalized, depressed youths at Center 2 received two-week precise rTMS. MRI and clinical assessments were obtained at baseline, midpoint, and treatment completion for evaluation.

RESULTS

Two neuroimaging subtypes of depression, archetypal and atypical depression, were identified based on distinct frontal-posterior functional imbalance patterns as measured by ALFF. The dorsomedial prefrontal cortex was identified as the rTMS target for archetypal depression, and the occipital cortex for atypical depression. Following precise rTMS, ALFF alterations were normalized in both archetypal and atypical depressed youths, corresponding with symptom response of 90.00% in archetypal depression and 70.73% in atypical depression.

CONCLUSIONS

A precision medicine framework for depression was developed based on objective neurobiomarkers and implemented with promising results, actualizing a subtyping-treatment-evaluation closed loop in depression. Future randomized controlled trials are warranted.

Effect of group-based vs individualized stimulation site selection on reliability of network-targeted TMS

BACKGROUND

Transcranial magnetic stimulation (TMS) is a widely used technique for the noninvasive assessment and manipulation of brain activity and behavior. Although extensively used for research and clinical purposes, recent studies have questioned the reliability of TMS findings because of the high inter-individual variability that has been observed.

OBJECTIVE

In this study, we compared the efficacy and reliability of different targeting scenarios on the TMS-evoked response.

METHODS

24 subjects underwent a single pulse stimulation protocol over two parietal nodes belonging to the Dorsal Attention (DAN) and Default Mode (DMN) Networks respectively. Across visits, the stimulated target for both networks was chosen either based on group-derived networks' maps or personalized network topography based on individual anatomy and functional profile. All stimulation visits were conducted twice, one month apart, during concomitant electroencephalography recording.

RESULTS

At the network level, we did not observe significant differences in the TMS-evoked response between targeting conditions. However, reliable patterns of activity were observed- for both networks tested- following the individualized targeting approach. When the same analyses were carried out at the electrode space level, evidence of reliable patterns was observed following the individualized stimulation of the DAN, but not of the DMN.

CONCLUSIONS

Our findings suggest that individualization of stimulation sites might ensure reliability of the evoked TMS-response across visits. Furthermore, individualized stimulation sites appear to be of foremost importance in highly variable, high order task-positive networks, such as the DAN.

Maximizing brain networks engagement via individualized connectome-wide target search

BACKGROUND

In recent years, the possibility to noninvasively interact with the human brain has led to unprecedented diagnostic and therapeutic opportunities. However, the vast majority of approved interventions and approaches still rely on anatomical landmarks and rarely on the individual structure of networks in the brain, drastically reducing the potential efficacy of neuromodulation.

OBJECTIVE

Here we implemented a target search algorithm leveraging on mathematical tools from Network Control Theory (NCT) and whole brain connectomics analysis. By means of computational simulations, we aimed to identify the optimal stimulation target(s)- at the individual brain level- capable of reaching maximal engagement of the stimulated networks' nodes.

RESULTS

At the model level, in silico predictions suggest that stimulation of NCT-derived cerebral sites might induce significantly higher network engagement, compared to traditionally employed neuromodulation sites, demonstrating NCT to be a useful tool in guiding brain stimulation. Indeed, NCT allows us to computationally model different stimulation scenarios tailored on the individual structural connectivity profiles and initial brain states.

CONCLUSIONS

The use of NCT to computationally predict TMS pulse propagation suggests that individualized targeting is crucial for more successful network engagement. Future studies will be needed to verify such prediction in real stimulation scenarios.

Transcranial magnetic stimulation treatment in Alzheimer's disease: a meta-analysis of its efficacy as a function of protocol characteristics and degree of personalization

Alzheimer's disease (AD) represents the most common type of neurodegenerative disorder. Although our knowledge on the causes of AD remains limited and no curative treatments are available, several interventions have been proposed in trying to improve patients' symptomatology. Among those, transcranial magnetic stimulation (TMS) has been shown a promising, safe and noninvasive intervention to improve global cognitive functioning. Nevertheless, we currently lack agreement between research studies on the optimal stimulation protocol yielding the highest efficacy in these patients. To answer this query, we conducted a systematic literature search in PubMed, PsycINFO and Scopus databases and meta-analysis of studies published in the last 10 years (2010-2021) according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Differently from prior published meta-analytic work, we investigated whether protocols that considered participants-specific neuroimaging scans for the selection of individualized stimulation targets held more successful outcomes compared to those relying on a generalized targeting selection criteria. We then compared the effect sizes of subsets of studies based on additional protocol characteristics (frequency, duration of intervention, number of stimulation sites, use of concomitant cognitive training and patients' educational level). Our results confirm TMS efficacy in improving global cognitive functioning in mild-to-moderate AD patients, but also highlight the flaws of current protocols characteristics, including a possible lack of sufficient personalization in stimulation protocols.

Effects of Noninvasive Brain Stimulation Combined With Antidepressants in Patients With Poststroke Depression: A Systematic Review and Meta-Analysis

Objective: To evaluated the efficacy and safety of noninvasive brain stimulation (NIBS) combined with antidepressants in patients with poststroke depression (PSD).

Methods: Seven databases were searched to identify randomized controlled trials of NIBS combined with antidepressants in the treatment of PSD based on the international classification of diseases (ICD-10) criteria and exclusion criteria. The retrieval time was from the database establishment to 31 October 2021. Two researchers independently screened the identified studies through the search strategy, extracted their characteristics, and evaluated the quality of the included literature. Cochrane Collaboration’s tool was used to assess risk of bias. RevMan 5.3 software was applied for meta-analysis.

Results: A total of 34 randomized controlled trials were included, involving 2,711 patients with PSD. Meta-analysis showed that the total effective rate was higher in the combined therapy than the antidepressant alone [odds ratio (OR): 4.33; 95% confidence interval (CI): 3.07 to 6.11; p < 0.00001]. The Hamilton depressive scale (HAMD) score was significantly lower in repeated transcranial magnetic stimulation (rTMS) (≤10 Hz) combined with antidepressant than in antidepressant alone [standard mean difference (SMD): −1.44; 95% CI: −1.86 to −1.03; p < 0.00001]. No significant difference was seen in rTMS (>10 Hz) combined with antidepressant versus antidepressant alone (SMD: −4.02; 95% CI: −10.43 to 2.39; p = 0.22). In addition, combination therapy more strongly improved the modified Barthel index (MBI) scale than antidepressants [mean difference (MD): 8.29; 95% CI: 5.23–11.35; p < 0.00001]. Adverse effects were not significantly different between two therapies (OR: 1.33; 95% CI: 0.87 to 2.04; p = 0.18).

Conclusion: Low-frequency rTMS (≤10 Hz) combined with antidepressants tends to be more effective than antidepressants alone in patients with PSD, and there are no significant adverse effects. In addition, combined therapy may enhance quality of life after stroke. Combination therapy with high-frequency rTMS (>10 Hz) showed no advantage in treating PSD. The transcranial electrical stimulation (TES) combined with antidepressants might be more effective than antidepressants alone, which are needed to confirm by more clinical trials since the.

Causal mapping of human brain function

Mapping human brain function is a long-standing goal of neuroscience that promises to inform the development of new treatments for brain disorders. Early maps of human brain function were based on locations of brain damage or brain stimulation that caused a functional change. Over time, this approach was largely replaced by technologies such as functional neuroimaging, which identify brain regions in which activity is correlated with behaviours or symptoms. Despite their advantages, these technologies reveal correlations, not causation. This creates challenges for interpreting the data generated from these tools and using them to develop treatments for brain disorders. A return to causal mapping of human brain function based on brain lesions and brain stimulation is underway. New approaches can combine these causal sources of information with modern neuroimaging and electrophysiology techniques to gain new insights into the functions of specific brain areas. In this Review, we provide a definition of causality for translational research, propose a continuum along which to assess the relative strength of causal information from human brain mapping studies and discuss recent advances in causal brain mapping and their relevance for developing treatments.

Mapping Inter-individual Functional Connectivity Variability in TMS Targets for Major Depressive Disorder

Transcranial magnetic stimulation (TMS) is an emerging alternative to existing treatments for major depressive disorder (MDD). The effects of TMS on both brain physiology and therapeutic outcomes are known to be highly variable from subject to subject, however. Proposed reasons for this variability include individual differences in neurophysiology, in cortical geometry, and in brain connectivity. Standard approaches to TMS target site definition tend to focus on coordinates or landmarks within the individual brain regions implicated in MDD, such as the dorsolateral prefrontal cortex (dlPFC) and orbitofrontal cortex (OFC). Additionally considering the network connectivity of these sites (i.e., the wider set of brain regions that may be mono- or poly-synaptically activated by TMS stimulation) has the potential to improve subject-specificity of TMS targeting and, in turn, improve treatment outcomes. In this study, we looked at the functional connectivity (FC) of dlPFC and OFC TMS targets, based on induced electrical field (E-field) maps, estimated using the SimNIBS library. We hypothesized that individual differences in spontaneous functional brain dynamics would contribute more to downstream network engagement than individual differences in cortical geometry (i.e., E-field variability). We generated individualized E-field maps on the cortical surface for 121 subjects (67 female) from the Human Connectome Project database using tetrahedral head models generated from T1- and T2-weighted MR images. F3 and Fp1 electrode positions were used to target the left dlPFC and left OFC, respectively. We analyzed inter-subject variability in the shape and location of these TMS target E-field patterns, their FC, and the major functional networks to which they belong. Our results revealed the key differences in TMS target FC between the dlPFC and OFC, and also how this connectivity varies across subjects. Three major functional networks were targeted across the dlPFC and OFC: the ventral attention, fronto-parietal and default-mode networks in the dlPFC, and the fronto-parietal and default mode networks in the OFC. Inter-subject variability in cortical geometry and in FC was high. Our analyses showed that the use of normative neuroimaging reference data (group-average or representative FC and subject E-field) allows prediction of which networks are targeted, but fails to accurately quantify the relative loading of TMS targeting on each of the principal networks. Our results characterize the FC patterns of canonical therapeutic TMS targets, and the key dimensions of their variability across subjects. The high inter-individual variability in cortical geometry and FC, leading to high variability in distributions of targeted brain networks, may account for the high levels of variability in physiological and therapeutic TMS outcomes. These insights should, we hope, prove useful as part of the broader effort by the psychiatry, neurology, and neuroimaging communities to help improve and refine TMS therapy, through a better understanding of the technology and its neurophysiological effects.

Effect of repetitive transcranial magnetic stimulation on anxiety symptoms in patients with major depression: An analysis from the THREE-D trial

BACKGROUND

Despite the advances in the use of repetitive transcranial magnetic stimulation (rTMS) for the treatment of major depressive disorder (MDD), there is relatively little information about its effect on comorbid anxiety symptoms.

METHODS

Data from a large randomized noninferiority trial comparing intermittent theta-burst stimulation (iTBS) and high-frequency (10 Hz) rTMS delivered to the left dorsolateral prefrontal cortex (HFL) were analyzed. The primary aim was assessing changes in anxiety/somatization items from the 17-item Hamilton Depression Rating Scale (HAM-D) and the Brief Symptom Inventory (BSI-A), using baseline-adjusted change with an analysis of covariance (ANCOVA), with the final scores as the outcome and baseline scores as the adjustment covariates.

RESULTS

The analytical cohort comprised 388 participants (189 in HFL and 199 in iTBS groups). From baseline to the end of the rTMS course, the combined score from the anxiety items from the HAM-D dropped from 7.43 (SD = 2.15) to 4.24 (SD = 2.33) in the HFL group, and 7.33 (SD = 2.13) to 3.76 (SD = 2.23) in the iTBS group. The ANCOVA resulted in an effect from time (p < .0001), but not from group allocation (p = .793) or time × group interaction (p = .976). We observed mean changes in the BSI-A of -3.5 (SD = 5.4) and -3.2 (SD = 4.8), with significant effect of time (p < .0001) in the ANCOVA, but not group allocation (p = .793) or group × time interaction (.664).

CONCLUSIONS

Our findings suggest that both 10 Hz and iTBS may yield potential reductions in anxiety symptoms when used for the treatment of MDD. Our findings warrant future research into the effects of left-sided rTMS on depressed patients struggling with concurrent anxiety symptoms.